Use of image sensors has recently become more widespread. Various techniques for custom calibration of intrinsic parameters of image sensors after manufacturing are commonly employed. Examples of the intrinsic parameters that are conventionally calibrated after manufacturing of the image sensor include focal lengths, lens distortions, offsets of optical axis centers, and so forth. Further, correction based on the calibration of the intrinsic parameters can be applied in real-time as the image sensor produces readings.
While various conventional approaches tend to calibrate the intrinsic parameters of the image sensors, conventional approaches oftentimes do not adequately address depth reading biases. Thus, depth sensors, many of which have limited accuracy upon leaving the factory floor, commonly report incorrect distances to objects as compared to distances to such objects measured by a precision distance measurement instrument (e.g., a laser range finder).
Depth sensors are commonly included in vision systems used for various applications that employ distances to objects on a scene. For example, depth sensors can be utilized to navigate in an indoor environment or manipulate objects on a tabletop. Depth sensors typically have limited accuracy when leaving the factory floor, which can be due to a combination of hardware capabilities and traditional calibration techniques for the depth sensors. For instance, many depth sensors are commonly calibrated on a per-sensor basis as part of the manufacturing process. The low level of accuracy commonly resulting from such conventional calibration techniques can be detrimental to overall system performance for applications that utilize the distances detected by the depth sensors, such as two-dimensional (2D) and three-dimensional (3D) scene reconstruction, 3D scanning, scene segmentation, robot navigation and manipulation, amongst others.